Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be...
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Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0-50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (lda) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of lda algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the lda algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the lda algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the lda algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.
A prevention and control tracking system based on three-dimensional (3D) face recognition was designed to improve the target tracking accuracy of the prevention and control tracking system. The ARM control chip of TMS...
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A prevention and control tracking system based on three-dimensional (3D) face recognition was designed to improve the target tracking accuracy of the prevention and control tracking system. The ARM control chip of TMS320DM6446 was selected as the control chip of the ARM control module. The CMOS image acquisition sensor of the image acquisition module collected face images. The collected images were transmitted to the 3D face recognition module. The 3D face recognition module used the Gabor wavelet algorithm to extract the 3D face contour features of the face image. Moreover, the lda algorithm was used to recognize faces based on 3D face contour features. The 3D face recognition results were compared with the faces in the face library to determine whether prevention and control tracking were necessary. When prevention and control tracking was needed, the GPS tracking and positioning module embedded in the mobile device terminal of the target object was started. The GPS tracking and positioning module was used to prevent and control the tracking of the target. The results of prevention and control tracking were displayed to the system users using a VGA display. The experimental results indicated that the designed system could accurately recognize faces and achieve prevention and control tracking of the target based on the face recognition results.
This study reports the results of a systematic literature review on auctions mechanism. Auctions are a very popular practice employed in many fields but does not exist a research that investigates the use of auctions ...
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This study reports the results of a systematic literature review on auctions mechanism. Auctions are a very popular practice employed in many fields but does not exist a research that investigates the use of auctions under a cross-disciplinary approach. This work is focused on analyzing which are the areas where auctions are mostly adopted and what are the techniques used to solve auction-related problems. Referring to the publications from 2013 to 2024 and using the latent Dirichlet allocation (lda) algorithm, three main topics related to auctions research are identified and widely explained. In addition, an auction classification scheme in which auction characteristics are classified along 10 different directions is developed and presented. The scheme is useful for understanding the auction building process, especially for those who approach the study of auctions. Promising future research directions on this subject are provided.
The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to...
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The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using lda algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.
The quality of myrrh decoction pieces can be influenced by factors such as origin, source, and processing methods. The quality of myrrh in the market varies greatly, and adulteration is a serious issue, highlighting t...
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The quality of myrrh decoction pieces can be influenced by factors such as origin, source, and processing methods. The quality of myrrh in the market varies greatly, and adulteration is a serious issue, highlighting the urgent need for improved quality control measures. This study explores the integration of GC-MS analysis and sensor selection in electronic nose technology for the improved classification of myrrh decoction pieces. GC-MS analysis revealed the presence of 130 volatile compounds in the six myrrh samples, primarily composed of alkene compounds, and each sample exhibited variations in composition. An electronic nose system was designed using a sensor array consisting of six sensors selected from twelve sensors capable of detecting volatile compounds consistent with myrrh composition, including WO3 quantum dots, Fe2O3 hollow nanorods, ZnFe2O4 nanorods, SnO2 nanowires, and two commercially available sensors. The sensors exhibited distinct response patterns to the myrrh samples, indicating their suitability for myrrh analysis. Various sensor parameters, including response, response and recovery time, integral area, and slope, were computed to characterize the sensors' performance. These parameters provided valuable insight into the sensor-gas interactions and the unique chemical profiles of the myrrh samples. The lda model demonstrated high accuracy in differentiating between the myrrh types, utilizing the discriminative features captured by the sensor array, with a classification accuracy of 90% on the testing set. This research provides a comprehensive evaluation method for the quality control of myrrh pieces and a scientific basis for the development and utilization of myrrh.
Background: Studies on competency in medical education often explore the acquisition, performance, and evaluation of particular skills, knowledge, or behaviors that constitute physician competency. As physician compet...
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Background: Studies on competency in medical education often explore the acquisition, performance, and evaluation of particular skills, knowledge, or behaviors that constitute physician competency. As physician competency reflects social demands according to changes in the medical environment, analyzing the research trends of physician competency by period is necessary to derive major research topics for future studies. Therefore, a more macroscopic method is required to analyze the core competencies of physicians in this era. Objective: This study aimed to analyze research trends related to physicians' competency in reflecting social needs according to changes in the medical environment. Methods: We used topic modeling to identify potential research topics by analyzing data from studies related to physician competency published between 2011 and 2020. We preprocessed 1354 articles and extracted 272 keywords. Results: The terms that appeared most frequently in the research related to physician competency since 2010 were knowledge, hospital, family, job, guidelines, management, and communication. The terms that appeared in most studies were education, model, knowledge, and hospital. Topic modeling revealed that the main topics about physician competency included Evidence-based clinical practice, Community-based healthcare, Patient care, Career and self-management, Continuous professional development, and Communication and cooperation. We divided the studies into 4 periods (2011-2013, 2014-2016, 2017-2019, and 2020-2021) and performed a linear regression analysis. The results showed a change in topics by period. The hot topics that have shown increased interest among scholars over time include Community-based healthcare, Career and self-management, and Continuous professional development. Conclusions: On the basis of the analysis of research trends, it is predicted that physician professionalism and community-based medicine will continue to be studied in future studies
This paper aims to analyze the microblog data published by the official account in a certain province of China,and finds out the rule of Weibo that is easier to be forwarded in the new police media *** this paper,a ne...
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This paper aims to analyze the microblog data published by the official account in a certain province of China,and finds out the rule of Weibo that is easier to be forwarded in the new police media *** this paper,a new topic-based model is ***,the lda topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers,then the Naive Bayesian algorithm is used to topic *** sample data is processed to predict the type of microblog *** order to evaluate this method,a large number of microblog online data is used to *** experimental results show that the proposed method can accurately predict the forwarding of Weibo.
The Principal Component Analysis (PCA) algorithm is widely used in the field of face recognition because of its high recognition rate and simplicity. The PCA algorithm is based on the principle of Karhunen-Loeve Trans...
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ISBN:
(纸本)9781450361910
The Principal Component Analysis (PCA) algorithm is widely used in the field of face recognition because of its high recognition rate and simplicity. The PCA algorithm is based on the principle of Karhunen-Loeve Transformation, because the PCA algorithm is sensitive to outliers, it is improved on the basis of PCA algorithm, combined with Linear Discriminant Analysis (lda) algorithm, the PCA-lda face recognition method is proposed. This method obtains the feature space of training sample set by PCA algorithm, On this basis, the lda algorithm is executed to obtain the feature space of fusion. The PCA is then fused with the lda's feature space to obtain the new feature space that combines the two. Finally, the face projected in the new feature space is trained and recognized. The experimental results show that the face recognition algorithm proposed in this paper has a higher recognition rate than the traditional PCA algorithm.
This paper is aimed at predicting microblogs' content and three kinds counts of behaviors that including forward counts, comment counts and like counts, which analyse the overall characteristics of microblog, and ...
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ISBN:
(纸本)9781450361286
This paper is aimed at predicting microblogs' content and three kinds counts of behaviors that including forward counts, comment counts and like counts, which analyse the overall characteristics of microblog, and propose an algorithm based on improved naive Bayesian-NB for predicting microblog behavior. Calculating microblogs' feature words based on TF*IDF, we classify microblog by lda algorithm, to find the right category set. Select feature words used frequently from microblog, as predictive properties, and construct an improved naive Bayesian algorithm for predicting. The result of experiment shows that the recall rate, precision rate and F1 evaluation value are improved.
The Principal Component Analysis (PCA) algorithm is widely used in the field of face recognition because of its high recognition rate and simplicity. The PCA algorithm is based on the principle of Karhunen-Loeve Trans...
详细信息
ISBN:
(纸本)9781450361910
The Principal Component Analysis (PCA) algorithm is widely used in the field of face recognition because of its high recognition rate and simplicity. The PCA algorithm is based on the principle of Karhunen-Loeve Transformation, because the PCA algorithm is sensitive to outliers, it is improved on the basis of PCA algorithm, combined with Linear Discriminant Analysis (lda) algorithm, the PCA-lda face recognition method is proposed. This method obtains the feature space of training sample set by PCA algorithm, On this basis, the lda algorithm is executed to obtain the feature space of fusion. The PCA is then fused with the lda's feature space to obtain the new feature space that combines the two. Finally, the face projected in the new feature space is trained and recognized. The experimental results show that the face recognition algorithm proposed in this paper has a higher recognition rate than the traditional PCA algorithm.
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